标签：#Startup

Reach Capital announced Tuesday an oversubscribed $265M Fund V.

The company has shifted from making van-life builds to custom outfitting vehicles for small businesses, all while the EV landscape in the US changed dramatically.

I’ve been building a side project called OopsCalorie , an AI-powered calorie and meal tracking app. The idea sounded simple enough: User logs or takes a photo of their food. AI identifies the meal. Estimate calories and macros. Save the entry. Done. Simple, right? Well... Then we started testing it with Filipino food. 😂 AI Meets Filipino Food 🇵🇭 One of the funniest parts of building OopsCalorie has been testing the food recognition. At one point, our AI confidently looked at dinuguan and decided: That's champorado. Okay. I can kind of see where you were coming from. Both are dark, both can be served in a bowl... But still. 😂 Then came bagnet . AI: Lumpiang Shanghai. Bro. Not even close. 😂 These bugs are funny, but they also exposed one of the more interesting engineering problems behind OopsCalorie: Image recognition is only the first step. Correctly identifying a meal — especially regional dishes — requires much more context than I initially expected. The Real Problem Isn't Just Calories When I started the project, I thought the difficult part would be estimating calories. Turns out, before you can estimate: You need to know what the food actually is. And food can be surprisingly ambiguous from an image. A photo might contain: multiple dishes sauces hiding ingredients visually similar foods regional dishes that aren't well represented in training data different cooking methods unknown portion sizes ingredients completely hidden underneath other ingredients Even humans sometimes need context. "Is that pork adobo or humba?" "Is that fried pork belly or bagnet?" Now imagine asking an AI to determine that from pixels alone. Building Around AI Instead of Blindly Trusting It This changed how I'm approaching the system. Instead of treating the AI response as absolute truth, OopsCalorie is evolving toward a workflow where AI provides an intelligent estimate while the user still has the ability to provide context and correct it. We're experimenting with things like: Image +

If you’ve been circling around Disrupt, then now’s the best time to lock in your pass and start getting ready to join the rest of the startup community gathering in San Francisco from October 13-15 at Moscone West!

Higgsfield, founded by former Snap exec Alex Mashrabov, lets users create AI images and videos.

African defense tech company Terra Industries announced an additional $18 million in funding, bringing its seed round to $52 million.

I'm building EverQuill, an AI-powered tabletop RPG platform — a game where an AI acts as your dungeon master, narrating the story and reacting to what you do. Every turn of that story is a message to a language model, and every message costs money. That cost isn't a problem you get to solve later, once you're big. It's a set of choices you make before anyone shows up, because the defaults you pick decide whether a bad day costs you a few cents or a few hundred dollars. Here are three decisions I made before I opened the alpha, and why. 1. When the tier is unclear, I reach for the stronger model This is tier-based routing : which model serves a request depends on who's asking. Better models write better stories and cost more; cheaper ones are faster and rougher. So free players get Claude Haiku (cheaper, ~$0.80 per million input tokens) and paying players get Claude Sonnet (stronger, ~$3.00 per million) — plus, because the platform is designed to route across providers, the same decision could hand a turn to a local model running through Ollama instead of a hosted one. That part is straightforward. The interesting case is the ambiguous one. My routing hands over the cheaper model only when a request is clearly free-tier; everything else — paid, alpha testers, admin, anything the system can't cleanly place as free — gets the stronger model. The instinct most people have is the reverse: default to cheap, protect the margin, don't spend money you don't have to. The reasoning: a rough, disappointing experience for someone who should have gotten a good one is a real cost — it just doesn't show up on the bill. It shows up as someone deciding the product isn't worth paying for. Meanwhile the money risk is capped elsewhere (more on that below), so biasing toward quality can't run away from me. Given a bounded downside on money and an unbounded downside on reputation, I'd rather err toward quality than toward cheap. 2. I stopped paying full price for the part that never chang

OpenRouter's CEO recently described the startup as Stripe for AI.

When many people are first learning how to code, they find it difficult and when they ask, "How can I get better at coding?" they are usually told, "With time and practise it will get easier." . But instead of using so much time and effort to get better at coding using hard programming languages, what if coding could get better for you instead of you getting better at coding ? Well, this is the reason that inspired me to make a new programming language called Sofya . Sofya is designed to be so simple (even simpler than Python ) so that anyone can find programming easy and fun. But to prove my point, let us use an example. Let us say that we want to make a program that will show us all the numbers from 1 to 20 . Let us compare how this program will look like in Python and Sofya . The Python Program for number in range ( 1 , 21 ): print ( number ) The Sofya Program Variable Number is 0 Do this { Increase Variable[Number] by 1 Write Variable[Number] on the screen } Until Variable[Number] = 20 From this example, we can see that the Sofya program is easier than the Python program, for a beginner in programming, for the following reasons: Sofya uses simpler commands than Python: It is easier for a beginner in programming to remember the command Do this...Until Variable[Number] = 20 , which is used for making a loop, as compared to the command for number in range(1, 21): . Sofya's syntax is closer to English as compared to Python's syntax: When we are making a loop variable in Sofya, we simply say Variable Number is 0 rather than saying number in range(1, 21) in Python. The Sofya program can easily be understood by anyone even if it is the first time that they are seeing it as compared to Python: A beginner in programming can easily tell that in the line where we say Increase Variable[Number] by 1 , that we are increasing the value of the variable called 'Number' by 1 as compared to the line number in range(1, 21) in Python. If you would like to try out Sofya for yourself

A few days ago I posted here about DevStacker, my app for helping self-taught developers escape tutorial hell and build real projects. We were getting ready to launch it, but then we found some bugs in the login screen. The app itself was working, but the login flow had some issues, and obviously we can't really launch while the first thing users see is broken lol. So we delayed the launch for a bit and we're fixing it now. I'm pretty close to getting everything ready, so hopefully DevStacker will be live very soon. This is also my first app, and honestly I didn't realize how many random things can go wrong until I actually tried to launch one 😭 Anyway, back to fixing login. I'll post again when DevStacker is finally live 🚀

Dario Amodei Is Right. But He Is Missing the Hard Part. Dario Amodei said the first billion-dollar company with one employee would appear in 2026. He put 70-80% probability on it. I am not building a billion-dollar company. But I am running something that does the work of several teams: 86 containers, 24 databases, 240 cron jobs, two servers, one person. Amodei is right that this is now possible. The tools exist. The costs dropped. A full AI stack costs me between $3,000 and $12,000 per year. The equivalent in human headcount would run $80,000 to $120,000 per month. But the headline version of the "one-person company" story skips the hard part. It sounds like you hire an AI, fire your team, and go make money. That is not what happened for me. What actually happened was eighteen months of building a system that makes "one person" sustainable at 3 AM when something breaks and nobody is awake to fix it. Here is what that system looks like in practice. The Stack Is Not the System Most people stop at the stack. They pick Claude or GPT, wire up a few automations, and call it an AI-powered business. That works until the first thing breaks in a way the model did not anticipate. The stack I run includes SaaS apps for golf clubs, a school management platform, an auth provider, a CRM, a community platform, and several tools for my own operations. Each of these runs in Docker containers managed by Coolify, spread across two Hetzner servers in Germany. That part is table stakes. Any competent developer can set up containers. The system is what sits on top. It is what makes the difference between "one person with a lot of tools" and "one person running a business that actually works." Guard Rules: The Thing That Catches What You Miss I wrote about this in detail in Runs Without Me : the biggest risk in a one-person setup is not that the AI does something wrong. It is that you do not notice until hours or days later. My setup uses 177 guard files that intercept operations before t

The story behind AI Clip Cutter — and why we’re building AI editing around one simple idea: the creator should stay in control. Press enter or click to view image in full size There is an uncomfortable truth about short-form content: Most creators don’t have a content problem. They have a time problem. You can spend an hour recording a podcast. Two hours researching. Three hours having a conversation worth sharing. And then discover that turning that one long video into five genuinely good Shorts is going to take another afternoon. Finding the moments. Cutting them. Reframing them. Writing captions. Making sure the captions don’t start halfway through a sentence. Checking whether the clip actually makes sense without the 30 seconds of conversation before it. Then doing it again. And again. And again. That was the problem that led us to build AI Clip Cutter. AI Clip Cutter But there was another question behind it: What if AI didn’t need to replace the editor? What if it could simply do the boring part incredibly well? The idea was simple Take a long-form video. Find the moments worth sharing. Turn them into short vertical clips. Add captions. Let the creator decide what gets published. Sounds obvious. But once we started building it, we realized that “find the best clips” is not actually a simple problem. A 60-minute podcast can contain dozens of technically valid 30-second sections. But most of them aren’t good Shorts. Some start in the middle of an argument. Some need 45 seconds of context. Some contain interesting information but have no hook. Some are emotional but say nothing. And some sound incredible when you’re sitting inside the full conversation — but completely confusing when they’re watched alone. So we needed the AI to understand something more important than: “What was said?” It needed to understand: “Would someone want to watch this?” We don’t ask AI to pick “interesting” moments This was one of our biggest product decisions. Instead of asking the mode

Spend an afternoon browsing new AI startups and a strange déjà vu sets in. The landing pages rhyme. There is a dark hero section, a gradient somewhere between indigo and violet, a little sparkle or star icon denoting Intelligence, a headline promising to let you “chat with” your documents or data or customers, and a demo video with the same upbeat, slightly anonymous soundtrack. You could swap the logos between fifty of these sites and almost nobody would notice. Sameness on the surface Some of this is just design fashion, and design fashions always converge. But the AI cohort has converged harder and faster than most, and the reason is worth naming: when everyone is building on top of the same handful of foundation models , the differentiation has to come from somewhere else , and branding is the cheapest lever to pull. If your product is a thin layer over a model anyone can call, you cannot differentiate on the model, so you differentiate on the gradient. When the engine is a commodity everyone rents, the paint job is the only thing left to argue about. Hence a thousand identical paint jobs. Funded by the same money, chasing the same story The uniformity runs deeper than design and architecture; it reaches into the incentives. A great many of these companies are funded by the same pools of venture capital, pitched against the same market maps, and steered toward the same narrative arc — explosive growth now, monetisation later, an acquisition or an IPO at the end. When the funding, the advice and the definition of success are shared, the strategies converge. Everyone chases the same enterprise customers, adopts the same land-grab pricing, and races the same clock, because that is the shape of company the money was betting on. This produces a cohort that is not only visually and technically alike but strategically alike, which makes the whole field unusually fragile to the same shocks. A shift in model pricing, a change in what the platform providers offer natively

AI coding startup Cursor is now officially a part of SpaceX.

Fusion startups have raised $7.1 billion to date, with the majority of it going to a handful of companies.

The idea that GPUs are poorly suited for agentic workflows may be a misconception, according to French startup Kog.

The plaintiffs say they invested nearly $1.2 million in the company, and are accusing Gomez of failing to build and market the startup.

AI is expensive, Ali Ghodsi tells TechCrunch. With so many investors wanting into his latest round, he said yes to more than planned.

Cognition may be looking to raise another mega round just a few months after raising $1 billion at a $26 billion valuation.

AI notetaking hardware has taken off over the past couple of years, with credit-card-sized devices, pendants, pins, and even transcribing earbuds all promising to capture your meetings and turn them into summaries and action items. Now, a whole wave of wearables — rings especially — are betting people want to capture stray thoughts and ideas the same way. One of […]